Method for Determining Support Points of a Test Plan

ABSTRACT

The invention relates to a method ( 1 ) for determining supporting points of a test plan ( 9 ) for measuring pre-defined test variables of a test machine on the basis of previously measured operating values ( 3 ) of operating variables of at least one field machine during the normal use thereof. In an aggregation step ( 2 ), the detected operating values ( 3 ) are allocated to categories ( 4 ) with regard to at least one selected operating variable, according to a predefined classification rule. Default variables are selected in a default step ( 5 ) before or after the aggregation step ( 2 ). The default variables form at least one subset of the operating variables. The operating category frequency ( 7 ) for each category ( 4 ) is determined in a determination step ( 6 ) following the aggregation step ( 2 ). In a subsequent determination step ( 8 ), the supporting points of the test plan ( 9 ) are determined on the basis of the operating category frequency ( 7 ). The supporting points are determined in the determination step ( 8 ) such that a deviation of a relative test category frequency, on the basis of the test plan ( 9 ), of determined default values of the default variables, associated with categories ( 4 ) according to the classification rules, from a relative operating category frequency of the operating values ( 3 ) classed according to the classification rules, of the operating variables corresponding to the default variables, is minimised according to a predefined optimisation criterion.

TECHNICAL FIELD

The disclosure relates to a method for determining support points of atest plan for measuring or simulating pre-defined test variables of atest machine.

BACKGROUND

Test plans are used in the measuring of certain characteristics of testmachines, for example internal combustion engines, transmissions, orentire vehicles, but test plans are also used in the measuring ofcertain characteristics of electric motors, generators and comparablemachines. With the aid of the test plans, test values of the pre-definedtest variables are meant to be measured and detected in a largevariation range of machine parameters, which influence the testvariables, in as short a time as possible. In addition, test planspermit the repetition of a test, in order to, for example, review andvalidate the already-determined test results. Test plans can also beused to establish characteristics of the test machines, through asimulation, on the basis of a mathematical model of the test machine.

Any parameters of the test machine come into consideration as testvariables. These parameters can either be directly detected bymeasurement or can be established on the basis of metrologicallydetected variables via mathematical models. If, for example, a motorvehicle is to be measured, the test variable can be the fuel consumptionof the motor vehicle or, for example, the damage level of differentcomponents of the motor vehicle, as well.

The test plans used for measuring comprise a sequence of support pointsfor each machine parameter of the test machine to be varied in the test.The test machine is controlled or regulated with the aid of thesesupport points such that the different operating parameters, at timepoints linked with the support points, assume machine parameter valueslikewise linked with the support points. Each support point thuscontains a time point information, as well as a machine parameter value.The support points can, for example, relate to speeds of a motor vehiclewhich this vehicle is to reach at certain time points. The sequence ofthe support points here forms a speed profile to be driven by the motorvehicle, or a route, provided that the transverse dynamics of the motorvehicle is taken into account.

Different standardized test plans are known, in particular fordetermining the fuel consumption of motor vehicles. A frequently-usedtest plan is the so-called NEDC Cycle.

Numerous methods to establish test plans are known from the prior art.The methods used are hereby usually selected according to the purposeintended with the measurement. For example, in the measuring of dynamiccharacteristics of the test machine, test plans are usually used, whichfundamentally differ from such test plans with which the test variablesare to be measured under stationary operating conditions. Known methodsfor creating test plans are, for example, summarized under the headings“design of experiments” and “statistical test planning”.

In addition, it is desirable in several areas to design test plans suchthat, through the test plans, a real usage profile of a fielded machineis obtained. This is desirable, for example in establishing the fuelconsumption of a motor vehicle, as the known standardized driving cyclesor test plans for the determining of the fuel consumption, such as theNEDC are based on rather unrealistic speed profiles. In addition, nomultidimensionality, such as for example the simultaneous considerationof the speed and the motor temperature, is taken into consideration. Tothat end, no methods are known from the prior art, which permit asimple, quickly adaptable creation of an, in particular multidimensionaltest plan, which achieves an actual use of the fielded machine in thetest machine.

SUMMARY

It is therefore seen as the object of the invention to provide a methodfor determining support points of a test plan, wherein the test plan, ifpossible, describes a real usage profile, but can be measured in asubstantially shorter time period than in the actual usage.

This object is achieved by a method for determining support points of atest plan for measuring or simulating pre-defined test variables of atest machine on the basis of previously metrologically establishedoperating values of operating variables of at least one fielded machineduring its intended use, wherein, in an aggregation step, the detectedoperating values are assigned, according to a pre-defined classificationrule with respect to one or multiple selected operating variables, toclasses, wherein, in a specification step, prior to or after theaggregation step, specification variables are selected, wherein thespecification variables form at least one subset of the operatingvariables, wherein, in an identification step following the aggregationstep, the frequency of operating classes for each class is determined,and wherein, in a subsequent determination step, the support points ofthe test plan are determined on the basis of the operating classfrequency, wherein the support points are determined in theidentification step such that a deviation of a relative test classfrequency, established on the basis of the test plan, and specificationvalues of the specification variables assigned to classes according tothe classification rule, is minimized, by a relative operating classfrequency of the operating values classified according to theclassification rule of the operating variables, corresponding to thespecification variables, according to a pre-defined optimizationcriterion. In this way, it is possible, for example, to establish thefuel consumption of a motor vehicle on the basis of realistic testplans. Additionally, for example also regional differences in the usageof motor vehicles and different usage profiles of buyer groups ofestablished motor vehicles can, in this way, be taken into account, inturn, with respect to the establishing of fuel consumption. The use oftest plans which map the actual usage of the fielded machine, can, forexample, however also be used for the design of components of newmachines, since, in this way, an entire life cycle of the new componentsinstalled in the test machine can be established on the basis of realusage profiles.

The test machine can for example be a newly developed motor vehicle. Theat least one fielded machine relates, for example, to a series-producedmotor vehicle used in normal traffic and comparable with the testmachine. The test variable can for example be a fuel consumption relatedto a 100 km drive. The operating variables are at least the operatingvariables of the motor vehicle significantly determining the fuelconsumption, for example the vehicle speed, the vehicle acceleration,the respectively selected gears, etc. The operating values of theseoperating variables are advantageously continuously detected during theuse of the motor vehicle, and/or are established on the basis ofmeasurement variables metrologically detected and processed with thehelp of mathematical models.

The operating values, directly metrologically determined or establishedwith the aid of mathematical models on the basis of measurement valuesare classified with respect to one or multiple selected operatingvariables. For example, the operating values of the operating variablesused for measuring the fuel consumption of a motor vehicle, e.g.actually driven speeds, are assigned to individual classes, such as forexample, 0-30 km/h, 31-50 km/h, 51-100 km/h, and >100 km/h, and in theestablishing step, the frequency for each of the classes with which therespective speed range was previously detected is determined.

On the basis of these frequency values, the test plan is subsequently orthe support points of the test plan are subsequently determined suchthat the relative test class frequency of specification valuesidentified on the basis of the test plan and assigned to classesaccording to the classification rule, of the specification variablescorrespond, as much as possible, to the respective relative operatingclass frequency. In this way, a test plan can be established, with whichspeeds are driven, which fall into the class 30-51 km/h and into theclass 0-30 km/h proportionally as often as the actually-establishedoperating values. The test class frequency is advantageously determinedwith the aid of a mathematical model of the test machine. It is,however, also possible and provided to establish the test classfrequency through the execution of tests with the test machine.

Insofar as the test plan is established on the basis of the operatingvalues of merely one fielded machine, the fuel consumption of the testmachine can, for example, advantageously be established on anindividualized customer basis, and the driving behavior of one of eachcustomer can be taken into consideration. Insofar as the goal of themeasurement, however, is the establishing of the test variables for acommon usage of the fielded machine, independent of individual fieldedmachines, it is advantageously provided that the operating values aredetected in a plurality of fielded machines.

The test plan determined with the aid of the disclosed method canadvantageously also be used to optimize operating strategies of a testmachine with the aid of representative user profiles and to optimallyparameterize the components of a test machine.

In particular insofar as the specification variables are already knownbefore the determination of the operating variables, it isadvantageously provided that the specification variables correspond tothe selected operating variables. In this way, the effort necessary forthe determination of the operating values can be reduced, as merelyoperating values must be established for operating variablescorresponding to the specification variables.

It is alternatively provided that all relevant operating values arecontinuously determined in the fielded machines and are transmitted onceor in regular intervals to a central database. In this way, thespecification variables can, at a later point in time, be flexiblyselected from the detected operating variables, and thus can be adaptedto the selection and thus to the respective requirements.

It is also possible and provided that, in place of the transmission ofthe operating values, exclusively the frequency values of alreadyclassified operating values are transmitted to the central database. Inthis way, the data quantity to be transmitted can be significantlyreduced. The aggregation step here already occurs, for example, on acontrol device of the various fielded machines. In this way, the privatesphere of the person using the fielded machine is also protected, sinceexclusively aggregated data are stored and further used. It is alsopossible and provided that the entire method is carried out on a controldevice of a fielded machine.

Advantageously, it is provided that the operating values are overwrittenand/or deleted directly after the aggregation step. The private sphereof the persons using the fielded machine can be particularly wellprotected in that the operating values are not stored.

In order to adapt the test plan determined with the disclosed method aswell as possible to the actual use profile, it is provided for that, inthe aggregation step, transition frequencies are determined for one ormultiple classified operating variables, wherein a transition frequencyis the number of the changes of an operating variable or multipleoperating variables of a class into a different class, and wherein, inthe determination step, the support points of the test plan aredetermined on the basis of the operating class frequency and thetransition frequencies, wherein the support points are established inthe identification step such that a deviation of a relative test classfrequency of the specification values identified on the basis of thetest plan and assigned to classes according to the classification rules,from a relative operating class frequency of the operating valuesclassified according to the classification rule, of the operatingvariables corresponding to the specification variables, as well as adeviation of test transition frequencies for the specification variablesfrom the transition frequencies for the operating variablescorresponding to the specification variables, is minimized according tothe pre-defined optimization criterion, wherein a test transitionfrequency is the number of changes of a specification variable ormultiple specification variables from one class into another class. Inthis way, the influence of the frequency of the operating point changeson the test variables can be taken into account in the determination ofthe test plan.

Through the use of the transition frequencies, how often and betweenwhich classes the driven speed changes, can, in the test planexemplarily described above, also be taken into account in addition tothe frequency of driven speeds. In this way, how long the operatingvariables are usually held in the respective classes, before anoperating value, new and assigned to another class is driven, isadditionally also considered. In addition, it is also possible byconsidering multiple operating variables, to take into accounttransition frequencies for the changing of multiple operating variablesof a class or a class combination into a different one. A classcombination is the classes of the operating variables taken intoaccount, which are assigned the operating values in the respectivestate.

It is also possible and provided that, in the aggregation step,transition possibilities for one or multiple classified operatingvariables are determined, wherein a transition possibility is atransition, the transition frequency of which is greater than zero,wherein a transition frequency is the number of changes of an operatingvariable or multiple operation variables from one class into anotherclass, wherein, in the determination step, the support points of thetest plan are identified on the basis of the operating class frequencyand the transition possibilities, wherein the support points areestablished in the determining step such that a deviation of a relativetest class frequency of specification values identified on the basis ofthe test plan and assigned to classes according to the classificationrule, of the operating variables, from a relative operating classfrequency of the operating values classified according to theclassification rule, of the operating variables corresponding to thespecification variables, is minimized according to the pre-definedoptimization criterion, and in that the test plan exclusively comprisessuch test transitions that are covered by the transition possibilities,wherein the test transitions are all changes of a specification variableor multiple specification variables from one class into another class.In this way, a test plan can be created on the basis of the operatingclass frequencies, which plan comprises transitions that are exclusivelypresent in the detected operating variables and therefore are physicallypossible.

To establish the support points of the test plan, it is advantageouslyprovided that the support points of the test plan are determined, in thedetermination step, with the aid of a Markov chain process, on the basisof the operating class frequencies and the transition frequencies.Classes can be selected, with the aid of Markov chain processes, on thebasis of the determined frequencies, in which classes the specificationvariables of the next support point of the test plan lie, wherein thetest plan, resulting with the aid of the Markov chain process, comprisestest class frequencies and test transition frequencies, which, in asufficiently-long test plan or in a test plan with a sufficient numberof support points, can, very strongly, approximate the operating classfrequencies or in particular the transition frequencies, or cancorrespond to these.

In order to be able to simply determine test plans, for example fordifferent test requirements, it is provided that support points of aninitial test plan are determined, in the determination step, with theaid of a Markov chain process, on the basis of the operating classfrequencies and the transition frequencies. The initial test plan isdetermined in the same manner and fashion, with the aid of the Markovchain process, as the previously-described test plan. However, theinitial test plan comprises especially many support points, and servesas the basis for the following production of one or multiple test plans.Advantageously, the initial test plan comprises 3 to 4 times as manysupport points as the test plan to be determined on the basis of initialtest plans, and particularly advantageously, 10 times as many supportplaces.

To establish different test plans from the initial test plan, it isprovided that, in the identification step subsequent to thedetermination of the initial test plan, the initial test plan issubdivided into initial test plan segments with the aid of asegmentation process. The initial test plan segments can then, with theaid of a suitable method, advantageously a random process, be combinedto one or multiple new test plans.

Advantageously, it is provided for that for the segmentation, for atleast one initial specification variable of the initial test plan,respectively at least one status value, is specified, and that theinitial test plan segments are generated such that each first and lastinitial specification value of each initial specification variable hasthe respective status value, for which at least one status value waspre-defined. In this way, it can, inter alia, be achieved that the testplans constituted from the initial test plan segments have a continuoussignal curve and no undesired transitions and in particular notransitions which are not included in the transition possibilities,between successive support points, on the places, at which differentinitial test plan segments were combined or composed with one another.

Advantageously, it is provided for that, respectively at least onestatus value is pre-defined for the segmentation, for at least twoinitial specification variables of the initial test plan, and that theinitial test plan segments are generated such that each first and lastinitial specification value comprises the respective status value ofeach initial specification variable, for which at least one status valuewas pre-defined. In this way and manner, multidimensional test plans canbe composed with the disclosed method in a simple manner. Through such asegmentation, multidimensional initial test plan segments can begenerated, wherein a continuous transition between the initial test plansegments, in the test plans generated on the basis of these initial testplan segments, is made possible. For example, the driven speed and theacceleration of the fielded machines are used, as operating variables,for the determination of a test plan for the measurement of a motorvehicle, and the respective operating values are assigned to classes inthe aggregation step, as well as the transition frequencies aredetermined. Subsequently, an initial test plan is created with the aidof the Markov chain process. This initial test plan is then segmentedwith the aid of the segmentation process, wherein the initial test plansegments are generated such that each first and last initial test valueof each initial specification variable, for which at least one statusvalue was pre-defined, comprises the respective status value. Forexample, for the segmentation, status values are pre-defined for theacceleration as well as also for the speed. A segmentation can hereresult, for example, if, in the initial test plan, the speed, as well asthe acceleration comprises one of the specified combinations of thestatus values for the speed and the acceleration. For example, theinitial test plan is segmented at the places at which the speed and theacceleration is zero and is segmented at the places at which the speedamounts to 10 km/h and the acceleration is zero.

The combinations of the status values for the segmentation areadvantageously pre-defined taking into account the operating classfrequencies, wherein the combinations of the status values areadvantageously selected from the classes, which comprise comparativelylarge operating class frequencies. In this way, numerous initial testplan segments can be composed.

Advantageously, it is provided for that multidimensional test plans arecreated with the aid of the disclosed method. The segmentation with thesegmentation process therefore occurs likewise in multiple dimensions orregarding multiple specification variables.

In order to determine one or multiple test plans on the basis of thedetermined initial test plan segments, it is provided for that,subsequently, the initial test plan segments are to be composedtogether, using a random process, to the test plan such that thepre-defined optimization criterion is minimized.

In a particularly advantageous configuration of the disclosed method, itis provided that numerous candidate test plans are subsequently composedfrom the initial test plan segments with at least one random process andthat subsequently, the candidate test plans are evaluated with respectto the optimization criterion and the candidate test plan whichminimizes the optimization criterion is selected as test plan. In thisway, suitable candidate test plans can be created on the basis of thepresent initial test plan segments with respect to different test planrequirements and the best, that is the test plan(s) possibly minimizingthe optimization criterion, is/are selected out of these candidate testplans.

To determine the support points of the test plan, it is advantageouslyprovided that, in an old test plan evaluation step, old test operatingvalues of old test operating variables are determined on the basis ofalready present old test plans, wherein the old test operating valuesform the operating values and, according to the classification rule, areassigned to classes, and wherein the support points are determined fromportions of the old test plans in the determination step, wherein theportions are selected such that a deviation of a relative test classfrequency of the test values determined on the basis of the test planand assigned to classes according to the classification rule, from arelative operating class frequency of the old test operating valuesclassified according to the classification rule, is minimized accordingto the specified optimization criterion. In this way, the new test plancan be composed from portions of one or multiple old test plans.

Advantageously, it is provided for that the determination of theportions of the old test plans occur with an as described-abovesegmentation process.

Advantageously, it is provided for that the old test operating valuesare determined at an old test machine comparable to the test machine. Inthis way, it can be achieved that the machine parameter values assignedto the support points can actually be achieved and driven by the testmachine.

Advantageously, it is provided that the old test operating values aredetermined metrologically. Advantageously, recordings of old testoperating variables detected in actual measurements are used for thispurpose.

It is, however, also possible and provided for that the old testoperating values are determined through a mathematical model of the testmachine or the old test machine. In this way, the effort to determinethe old test operating values can be significantly reduced and, also,old test plans can be used to establish the support points of the testplan, for which no actually-determined operating variables are given.

Advantageously, it is provided for that the test values are determinedthrough a mathematical model of the test machine. Advantageously,operating parameters of the test machine, metrologically-detected, inthe measurement, with the test plan, are processed in the mathematicalmodel.

Further advantageous configurations of the method are explained ingreater detail by means of exemplary embodiments illustrated in thedrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematically represented flow diagram of the method fordetermining support points of a test plan.

FIG. 2 is a schematically represented flow diagram of the aggregationstep.

FIG. 3 is a schematically represented flow diagram of the identificationstep using old test plans.

FIG. 4 is a schematically represented flow diagram of the identificationstep using a parameterizable standard test plan.

FIG. 5 is a schematic representation of a conjunction between operatingclass frequencies and transition frequencies.

FIG. 6 is a schematic representation of the flow of a segmentationprocess.

DETAILED DESCRIPTION

FIG. 1 schematically shows a flow diagram of a method 1 for determiningsupport points of a test plan. In an aggregation step 2,metrologically-established operating values 3 of at least one fieldedmachine are assigned to classes 4 according to a pre-definedclassification rule. In a specification step 5 subsequent to theaggregation step 2, specification variables are selected. Thespecification variables are operating variables of the test machinewhich have a relevant influence on the test variables and thereforeshould be varied, corresponding to the test plan to be established, inthe measurement of the test machine.

Subsequently, the operating class frequency is determined in adetermination step 6 for each class 4, into which the operating values 3were previously categorized. On the basis of the thus-determinedoperating class frequencies 7, a test plan 9 is established in anidentification step 8, with the aid of a suitable optimization process.

FIG. 2 schematically shows a possible sequence of the aggregation step2. Initially, operating values 3 are metrologically detected fromoperating variables 10 of multiple fielded machines 11. The operatingvariables 10 relate to the speed 12 and the acceleration 13 of differentmotor vehicles.

Subsequently, the detected operating values 3 are assigned, according toa pre-defined classification rule, to classes 4, with respect to speed12 and acceleration 13. The assignment, occurring in each caseseparately for the fielded machines 11 is subsequently brought together,and the thus-determined operation class frequencies 7 are stored in adatabase 14. How long the fielded machines 11 have respectively driventhe classified speed and acceleration combinations can be taken from theoperating class frequencies 7.

FIG. 3 schematically illustrates the sequence of an identification step8. The support points 16 of a test plan 9 are determined from portions17 of old test plans 15, in the identification step 8, on the basis ofalready-present old test plans 15.

FIG. 4 shows a schematically-represented flow diagram of theidentification step 8 using a parameterizable standard test plan 18. Thestandard test plan 18 comprises a pre-defined sequence of the speed 12.Speed values 19, as well as time points 20, at which the speed values 19are supposed to have been reached, can be specified in variationportions 21 of the standard test plan 18. A time duration 22 of aspecified portion 23 can, in this example, not be specified. It is alsopossible, however, and provided for that all parameters are freelyselectable. Through a suitable optimization process, the speed values 19and the time points 20 of the standard test plan 18 are adapted suchthat a deviation of a relative test class frequency of specificationvalues of the specification variables identified on the basis of thetest plan and assigned to classes according to the classification rule,from a relative operating class frequency of the operating values,classified according to the classification rule, of the operatingvariables corresponding to the specification values is minimizedaccording to a specified optimization criterion.

It is alternatively also possible to identify the support points, aswell as the number of the support points, using a suitable optimizationprocess, starting from zero.

FIG. 5 shows a schematic illustration of the relation between theoperating class frequencies 7 (H) and the transition frequencies 24 (T).In the illustration, individual operating class frequencies 7 andtransition frequencies 24 are denoted by a reference character.

In an aggregation step, operating values of operating variables 10 ofmultiple field machines were initially metrologically detected. Theoperating variables 10 relate to the speed v and acceleration a ofmultiple motor vehicles.

Subsequently, the detected operating values were assigned to classes 4according to a predefined classification rule with respect to the speedv and the acceleration a. In the illustration, individual classes 4 aredenoted by a reference character.

In addition, multiple transition frequencies 24 were identified for eachclass 4. The drawing, by way of example, illustrates transitionfrequencies T of a class H_(31,5). Depending on a change in accelerationΔa and a change in speed Δv, the transition frequencies T indicate howoften the operating values, based upon class H_(31,5), change inaccordance with the changes in acceleration Δa, and the change in speedΔv.

FIG. 6 is a schematic illustration of the principle of a segmentationprocess. In an identification step, an initial test plan 25 wasestablished using a Markov chain process. Initial specificationvariables 26 of the initial test plan 25 are an acceleration a and aspeed v.

Initial test plan segments 27 are to be identified on the basis of theinitial test plan 25. Status values 28 of the initial specificationvariables 26 were predefined for the segmentation. The segmentation isin each case to be effected at an acceleration a=0 m/s² and a speed ofeither v=0 km/h or v=8 km/h. At these places (a=0 m/s² and v=0 km/h; a=0m/s² and v=8 km/h), the initial test plan 25 is respectivelysub-divided. Support points 16 of the initial test plan 25, which arebetween two subdivision points 29, together form an initial test plansegment 27.

1. A method (1) for determining support points (16) of a test plan (9)for measuring pre-defined test variables of a test machine based onpreviously measured operating values (3) of operating variables of atleast one fielded machine (11) during its intended use, wherein, in anaggregation step (2), the operating values (3) are assigned to classes(4) with respect to one or more selected operating variables, accordingto a predefined classification rule, wherein specification variables areselected in a specification step (5) prior to or after the aggregationstep (2), wherein the specification variables form at least one subsetof the operating variables, wherein, in a determination step (6)following the aggregation step (2), an operating class frequency (7) isidentified for each class (4), and wherein in a subsequentidentification step (8) the support points (16) of the test plan (9) areidentified on the basis of the operating class frequency (7), whereinthe support points (16) are determined in the identification step (8)such that a deviation of a relative test class frequency ofspecification values of the specification variables identified on thebasis of the test plan (9) and assigned to classes (4) according to theclassification rule, from a relative operating class frequency of theoperating values (3), classified according to the classification rule,of the operating variables corresponding to the specification values isminimized according to a specified optimization criterion.
 2. The method(1) according to claim 1, characterized in that the operating values (3)are overwritten and/or deleted directly after the aggregation step (2).3. The method (1) according to claim 1, characterized in that theoperating values (3) are detected in a plurality of fielded machines(11).
 4. The method (1) according to claim 1, characterized in that thespecification variables correspond to the selected operating variables.5. The method (1) according to claim 1, characterized in that in theaggregation step (2), transition frequencies are identified for one ormultiple classified operation variables, wherein a transition frequency(24) is the number of changes of an operating variable or multipleoperating variables from one class (4) to another class (4), and whereinin the identification step (8), the support points (16) of the test plan(9) are identified on the basis of the operating class frequency (7) andthe transition frequencies (24), wherein the support points (16) areidentified in the identification step (8) in such a way that a deviationof a relative test class frequency of specification values of thespecification variables identified on the basis of the test plan (9) andassigned to classes (4) according to the classification rule, from arelative operating class frequency of the operating values (3),classified according to the classification rule, of the operatingvariables corresponding to the specification values, and a deviation oftest transition frequencies for the specification variables from thetransition frequencies for the operating variables corresponding to thespecification variables is minimized according to the specifiedoptimization criterion, wherein a test transition frequency is thenumber of changes of a specification variable or of multiplespecification variables from one class (4) to another class (4).
 6. Themethod (1) according to claim 5, characterized in that in theidentification step (8), the support points (16) of the test plan (9)are identified, using a Markov chain process, on the basis of theoperating class frequencies (7) and the transition frequencies (24). 7.The method (1) according to claim 5, characterized in that in theidentification step (8), support points (16) of an initial test plan(25) are identified, using a Markov chain process, on the basis of theoperating class frequency (7) and the transition frequencies (24). 8.The method (1) according to claim 7, characterized in that in theidentification step (8), subsequent to the determination of the initialtest plan (25), the initial test plan (25) is sub-divided intoone-dimensional or multi-dimensional initial test plan segments (27)using a segmentation process.
 9. The method (1) according to claim 8,characterized in that for the segmentation, in each case at least onestatus value (28) is predefined for at least one initial specificationvariable (26) of the initial test plan (25), and in that the initialtest plan segments (27) are generated such that each first and each lastinitial specification value of each initial specification variable (26),for which at least one status value (28) was predefined, has therespective status value (28).
 10. The method (1) according to claim 9,characterized in that for the segmentation, in each case at least onestatus value (28) is specified for at least two initial specificationvariables (26) of the initial test plan (25), and in that the initialtest plan segments (27) are generated such that each first and each lastinitial specification value of each initial specification variable (26),for which at least one status value (28) was predefined, has therespective status value (28).
 11. The method (1) according to claim 8,characterized in that subsequently, the initial test plan segments (27)are composed to form the test plan (9) using a random process in such away, that the specified optimization criterion is minimized.
 12. Themethod (1) according to claim 8, characterized in that subsequently,numerous candidate test plans are generated from the initial test plansegments (27) using at least one random process, and in thatsubsequently, the candidate test plans are rated in view of theoptimization criterion and the candidate test plan is selected as thetest plan (9) that minimizes the optimization criterion.
 13. The method(1) according to claim 1, characterized in that in an old testevaluation step on the basis of already present old test plans (15), oldtest operating values of old test operating variables are identified,wherein the old test operating values form the operating values (3) andare assigned to classes (4) according to the classification rule, andwherein in the identification step (8) the support points (16) areidentified from portions (17) of the old test plans (15), wherein theportions (17) are selected such that a deviation of a relative testclass frequency of specification values identified on the basis of thetest plan (9) and assigned to classes (4) according to theclassification rule, from a relative operating class frequency of theold test operating values classified according to the classificationrule, is minimized according to the specified optimization criterion.14. The method (1) according to claim 13, characterized in that the oldtest operating values are identified on an old test machine comparableto the test machine.
 15. The method (1) according to claim 13,characterized in that the old test operating values are identified bymeasuring.
 16. The method (1) according to claim 13, characterized inthat the old test operating values are identified by a mathematicalmodel of the test machine or of the old test machine.
 17. The method (1)according to claim 1, characterized in that the specification values areidentified by a mathematical model of the test machine.